Correlations with general moral concern
##
## Pearson's product-moment correlation
##
## data: MR1 and concern_general
## t = 4.4105, df = 229, p-value = 1.587e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.156382 0.394636
## sample estimates:
## cor
## 0.2798115
##
## Pearson's product-moment correlation
##
## data: MR2 and concern_general
## t = 3.4838, df = 229, p-value = 0.0005921
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09811329 0.34348073
## sample estimates:
## cor
## 0.2243498
##
## Pearson's product-moment correlation
##
## data: MR3 and concern_general
## t = 0.047144, df = 229, p-value = 0.9624
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1260130 0.1321399
## sample estimates:
## cor
## 0.003115362
##
## Pearson's product-moment correlation
##
## data: MR1 and MR2
## t = -0.13729, df = 229, p-value = 0.8909
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1379882 0.1201462
## sample estimates:
## cor
## -0.009072134
##
## Pearson's product-moment correlation
##
## data: MR1 and MR3
## t = 0.48221, df = 229, p-value = 0.6301
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09762977 0.16026807
## sample estimates:
## cor
## 0.03184926
##
## Pearson's product-moment correlation
##
## data: MR2 and MR3
## t = 0.040367, df = 229, p-value = 0.9678
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1264537 0.1316998
## sample estimates:
## cor
## 0.0026675
## Call: paired.r(xy = rp_MR1.gen$estimate, xz = rp_MR2.gen$estimate,
## yz = rp_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 0.62 With probability = 0.53
## Call: paired.r(xy = rp_MR1.gen$estimate, xz = rp_MR3.gen$estimate,
## yz = rp_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 3.12 With probability = 0
## Call: paired.r(xy = rp_MR2.gen$estimate, xz = rp_MR3.gen$estimate,
## yz = rp_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 2.42 With probability = 0.02
## Warning in cor.test.default(MR1, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and concern_general
## S = 1435500, p-value = 3.123e-06
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3012511
## Warning in cor.test.default(MR2, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR2 and concern_general
## S = 1682900, p-value = 0.005847
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1808292
## Warning in cor.test.default(MR3, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR3 and concern_general
## S = 2162500, p-value = 0.4261
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.05261743
## Warning in cor.test.default(MR1, MR2, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and MR2
## S = 2476500, p-value = 0.001691
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2054888
## Warning in cor.test.default(MR1, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and MR3
## S = 2115200, p-value = 0.6541
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.0296376
## Warning in cor.test.default(MR2, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR2 and MR3
## S = 2106800, p-value = 0.6994
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.0255419
## Call: paired.r(xy = rs_MR1.genrank$estimate, xz = rs_MR2.genrank$estimate,
## yz = rs_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 1.24 With probability = 0.22
## Call: paired.r(xy = rs_MR1.genrank$estimate, xz = rs_MR3.genrank$estimate,
## yz = rs_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 3.9 With probability = 0
## Call: paired.r(xy = rs_MR2.genrank$estimate, xz = rs_MR3.genrank$estimate,
## yz = rs_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 2.51 With probability = 0.01
##
## Pearson's product-moment correlation
##
## data: MR1 and concern_general
## t = 3.1616, df = 119, p-value = 0.001991
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1050907 0.4352364
## sample estimates:
## cor
## 0.2783656
##
## Pearson's product-moment correlation
##
## data: MR2 and concern_general
## t = 3.042, df = 119, p-value = 0.002892
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.09464914 0.42664831
## sample estimates:
## cor
## 0.2686077
##
## Pearson's product-moment correlation
##
## data: MR3 and concern_general
## t = 0.0061249, df = 119, p-value = 0.9951
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1779529 0.1790401
## sample estimates:
## cor
## 0.0005614675
##
## Pearson's product-moment correlation
##
## data: MR1 and MR2
## t = 13.648, df = 119, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.7002746 0.8422012
## sample estimates:
## cor
## 0.7811323
##
## Pearson's product-moment correlation
##
## data: MR1 and MR3
## t = 0.22997, df = 119, p-value = 0.8185
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1580147 0.1988248
## sample estimates:
## cor
## 0.0210763
##
## Pearson's product-moment correlation
##
## data: MR2 and MR3
## t = -0.52753, df = 119, p-value = 0.5988
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2248602 0.1313264
## sample estimates:
## cor
## -0.04830237
## Call: paired.r(xy = rp_ROBOT_MR1.gen$estimate, xz = rp_ROBOT_MR2.gen$estimate,
## yz = rp_ROBOT_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 0.23 With probability = 0.82
## Call: paired.r(xy = rp_ROBOT_MR1.gen$estimate, xz = rp_ROBOT_MR3.gen$estimate,
## yz = rp_ROBOT_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 3.11 With probability = 0
## Call: paired.r(xy = rp_ROBOT_MR2.gen$estimate, xz = rp_ROBOT_MR3.gen$estimate,
## yz = rp_ROBOT_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 2.89 With probability = 0
## Warning in cor.test.default(MR1, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and concern_general
## S = 229350, p-value = 0.01387
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2231781
## Warning in cor.test.default(MR2, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR2 and concern_general
## S = 209340, p-value = 0.001205
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2909529
## Warning in cor.test.default(MR3, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR3 and concern_general
## S = 311950, p-value = 0.5376
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.05658593
## Warning in cor.test.default(MR1, MR2, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and MR2
## S = 231830, p-value = 0.018
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.2147718
## Warning in cor.test.default(MR1, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and MR3
## S = 283930, p-value = 0.6765
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.03831133
## Warning in cor.test.default(MR2, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR2 and MR3
## S = 360410, p-value = 0.01498
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.220733
## Call: paired.r(xy = rs_ROBOT_MR1.genrank$estimate, xz = rs_ROBOT_MR2.genrank$estimate,
## yz = rs_ROBOT_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = -0.86 With probability = 0.39
## Call: paired.r(xy = rs_ROBOT_MR1.genrank$estimate, xz = rs_ROBOT_MR3.genrank$estimate,
## yz = rs_ROBOT_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 3.13 With probability = 0
## Call: paired.r(xy = rs_ROBOT_MR2.genrank$estimate, xz = rs_ROBOT_MR3.genrank$estimate,
## yz = rs_ROBOT_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 3.49 With probability = 0
##
## Pearson's product-moment correlation
##
## data: MR1 and concern_general
## t = 1.1775, df = 108, p-value = 0.2416
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07626003 0.29364023
## sample estimates:
## cor
## 0.1125891
##
## Pearson's product-moment correlation
##
## data: MR2 and concern_general
## t = 2.4254, df = 108, p-value = 0.01695
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.04181352 0.39759715
## sample estimates:
## cor
## 0.2272756
##
## Pearson's product-moment correlation
##
## data: MR3 and concern_general
## t = 1.5122, df = 108, p-value = 0.1334
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04444539 0.32253964
## sample estimates:
## cor
## 0.1439944
##
## Pearson's product-moment correlation
##
## data: MR1 and MR2
## t = -1.7993, df = 108, p-value = 0.07477
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.34676327 0.01719213
## sample estimates:
## cor
## -0.1705985
##
## Pearson's product-moment correlation
##
## data: MR1 and MR3
## t = 17.792, df = 108, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.8066654 0.9044886
## sample estimates:
## cor
## 0.863485
##
## Pearson's product-moment correlation
##
## data: MR2 and MR3
## t = 0.28544, df = 108, p-value = 0.7759
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1606108 0.2135997
## sample estimates:
## cor
## 0.02745637
## Call: paired.r(xy = rp_BEETLE_MR1.gen$estimate, xz = rp_BEETLE_MR2.gen$estimate,
## yz = rp_BEETLE_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = -1.16 With probability = 0.25
## Call: paired.r(xy = rp_BEETLE_MR1.gen$estimate, xz = rp_BEETLE_MR3.gen$estimate,
## yz = rp_BEETLE_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = -0.92 With probability = 0.36
## Call: paired.r(xy = rp_BEETLE_MR2.gen$estimate, xz = rp_BEETLE_MR3.gen$estimate,
## yz = rp_BEETLE_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 0.93 With probability = 0.35
## Warning in cor.test.default(MR1, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and concern_general
## S = 199790, p-value = 0.302
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.09930608
## Warning in cor.test.default(MR2, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR2 and concern_general
## S = 177680, p-value = 0.03715
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.1989936
## Warning in cor.test.default(MR3, concern_general, method = "spearman"):
## Cannot compute exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR3 and concern_general
## S = 2e+05, p-value = 0.3066
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.09836269
## Warning in cor.test.default(MR1, MR2, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and MR2
## S = 286800, p-value = 0.001898
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.2929481
## Warning in cor.test.default(MR1, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR1 and MR3
## S = 35168, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.8414527
## Warning in cor.test.default(MR2, MR3, method = "spearman"): Cannot compute
## exact p-value with ties
##
## Spearman's rank correlation rho
##
## data: MR2 and MR3
## S = 240360, p-value = 0.3852
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.08360158
## Call: paired.r(xy = rs_BEETLE_MR1.genrank$estimate, xz = rs_BEETLE_MR2.genrank$estimate,
## yz = rs_BEETLE_MR1.MR2$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = -0.96 With probability = 0.34
## Call: paired.r(xy = rs_BEETLE_MR1.genrank$estimate, xz = rs_BEETLE_MR3.genrank$estimate,
## yz = rs_BEETLE_MR1.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 0.03 With probability = 0.98
## Call: paired.r(xy = rs_BEETLE_MR2.genrank$estimate, xz = rs_BEETLE_MR3.genrank$estimate,
## yz = rs_BEETLE_MR2.MR3$estimate, n = d_moral_merged_n)
## [1] "test of difference between two correlated correlations"
## t = 1.06 With probability = 0.29
Regressions with general moral concern
## Analysis of Variance Table
##
## Model 1: concern_general ~ MR1 + MR2 + MR3 + condition
## Model 2: concern_general ~ (MR1 + MR2 + MR3) * condition
## Model 3: concern_general ~ (MR1 + MR2 + MR3 + condition)^2
## Model 4: concern_general ~ (MR1 + MR2 + MR3)^2 * condition
## Model 5: concern_general ~ (MR1 + MR2 + MR3)^3 * condition
## Model 6: concern_general ~ (MR1 + MR2 + MR3 + condition)^3
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 226 111315
## 2 223 111154 3 161.2 0.1135 0.952138
## 3 220 104031 3 7123.2 5.0166 0.002213 **
## 4 217 103464 3 567.1 0.3994 0.753576
## 5 215 101761 2 1702.3 1.7983 0.168065
## 6 216 102059 -1 -297.6 0.6288 0.428680
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = concern_general ~ (MR1 + MR2 + MR3 + condition)^2,
## data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -49.836 -14.847 -4.775 11.270 68.561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 34.9356 6.5930 5.299 2.82e-07 ***
## MR1 17.7455 6.7744 2.620 0.009419 **
## MR2 5.9861 2.5214 2.374 0.018448 *
## MR3 -0.1649 2.7975 -0.059 0.953046
## conditionrobot 11.3630 6.5462 1.736 0.083995 .
## MR1:MR2 -12.1356 3.3463 -3.627 0.000357 ***
## MR1:MR3 1.0616 2.9577 0.359 0.719994
## MR1:conditionrobot 12.4343 6.9827 1.781 0.076336 .
## MR2:MR3 1.8989 2.7016 0.703 0.482873
## MR2:conditionrobot -10.2120 3.7136 -2.750 0.006457 **
## MR3:conditionrobot 1.3525 3.8584 0.351 0.726268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.75 on 220 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1868, Adjusted R-squared: 0.1499
## F-statistic: 5.055 on 10 and 220 DF, p-value: 1.26e-06
## Analysis of Variance Table
##
## Model 1: concern_general ~ (MR1 + MR2 + condition)^2
## Model 2: concern_general ~ (MR1 + MR2)^2 * condition
## Model 3: concern_general ~ (MR1 + MR2 + condition)^3
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 224 104328
## 2 223 103786 1 541.91 1.1644 0.2817
## 3 223 103786 0 0.00
## Analysis of Variance Table
##
## Model 1: concern_general ~ (MR1 + MR2 + condition)^2
## Model 2: concern_general ~ (MR1 + MR2 + MR3 + condition)^2
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 224 104328
## 2 220 104031 4 296.69 0.1569 0.9597
##
## Call:
## lm(formula = concern_general ~ (MR1 + MR2 + condition)^2, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -47.813 -14.897 -4.131 11.159 68.378
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 35.224 5.247 6.713 1.55e-10 ***
## MR1 16.147 5.840 2.765 0.006167 **
## MR2 6.077 2.450 2.480 0.013862 *
## conditionrobot 9.455 5.241 1.804 0.072565 .
## MR1:MR2 -11.004 2.874 -3.829 0.000167 ***
## MR1:conditionrobot 12.299 6.011 2.046 0.041907 *
## MR2:conditionrobot -8.899 3.203 -2.779 0.005921 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.58 on 224 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1845, Adjusted R-squared: 0.1627
## F-statistic: 8.447 on 6 and 224 DF, p-value: 2.84e-08
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

## Analysis of Variance Table
##
## Model 1: concern_general ~ rank(MR1) + rank(MR2) + rank(MR3) + condition
## Model 2: concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3)) * condition
## Model 3: concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3) + condition)^2
## Model 4: concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3))^2 * condition
## Model 5: concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3))^3) * condition
## Model 6: concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3) + condition)^3)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 227 111333
## 2 224 110964 3 369.09 0.2534 0.85885
## 3 221 108699 3 2265.00 1.5552 0.20128
## 4 218 106584 3 2114.86 1.4521 0.22865
## 5 216 104864 2 1720.14 1.7716 0.17253
## 6 217 106369 -1 -1504.51 3.0990 0.07976 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = concern_general ~ rank(MR1) + rank(MR2) + rank(MR3) +
## condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.108 -16.259 -5.906 11.442 74.814
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.953613 5.558039 0.531 0.595652
## rank(MR1) 0.097392 0.041082 2.371 0.018593 *
## rank(MR2) 0.082199 0.022388 3.671 0.000301 ***
## rank(MR3) 0.004384 0.024329 0.180 0.857143
## conditionrobot -1.471971 2.890813 -0.509 0.611114
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.15 on 227 degrees of freedom
## Multiple R-squared: 0.1323, Adjusted R-squared: 0.117
## F-statistic: 8.653 on 4 and 227 DF, p-value: 1.619e-06
##
## Call:
## lm(formula = concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3)) *
## condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.034 -16.302 -5.349 11.421 74.219
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.027891 8.105578 -0.127 0.899202
## rank(MR1) 0.109456 0.057459 1.905 0.058069 .
## rank(MR2) 0.095008 0.027387 3.469 0.000626 ***
## rank(MR3) -0.005839 0.035403 -0.165 0.869140
## conditionrobot -1.475383 8.105578 -0.182 0.855731
## rank(MR1):conditionrobot -0.029772 0.057459 -0.518 0.604872
## rank(MR2):conditionrobot 0.016899 0.027387 0.617 0.537815
## rank(MR3):conditionrobot 0.019313 0.035403 0.546 0.585941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.26 on 224 degrees of freedom
## Multiple R-squared: 0.1352, Adjusted R-squared: 0.1082
## F-statistic: 5.002 on 7 and 224 DF, p-value: 2.776e-05
##
## Call:
## lm(formula = concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3) +
## condition)^2, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -39.501 -15.142 -6.292 12.595 71.322
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.700e+01 1.674e+01 -1.613 0.10813
## rank(MR1) 2.386e-01 1.487e-01 1.605 0.10988
## rank(MR2) 2.535e-01 9.641e-02 2.629 0.00916 **
## rank(MR3) 1.334e-01 1.095e-01 1.218 0.22436
## conditionrobot -1.065e+00 9.916e+00 -0.107 0.91453
## rank(MR1):rank(MR2) -5.417e-04 8.297e-04 -0.653 0.51453
## rank(MR1):rank(MR3) -5.129e-04 6.595e-04 -0.778 0.43756
## rank(MR1):conditionrobot -1.286e-02 6.472e-02 -0.199 0.84270
## rank(MR2):rank(MR3) -6.356e-04 4.889e-04 -1.300 0.19496
## rank(MR2):conditionrobot 7.947e-03 5.537e-02 0.144 0.88601
## rank(MR3):conditionrobot 4.267e-03 5.730e-02 0.074 0.94070
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.18 on 221 degrees of freedom
## Multiple R-squared: 0.1528, Adjusted R-squared: 0.1145
## F-statistic: 3.987 on 10 and 221 DF, p-value: 5.01e-05
##
## Call:
## lm(formula = concern_general ~ (rank(MR1) + rank(MR2) + rank(MR3))^2 *
## condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.824 -14.748 -5.485 12.081 69.684
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.021e+01 2.069e+01 -1.460 0.1456
## rank(MR1) 1.940e-01 1.615e-01 1.201 0.2310
## rank(MR2) 2.964e-01 1.314e-01 2.257 0.0250
## rank(MR3) 8.293e-02 1.367e-01 0.607 0.5446
## conditionrobot 2.971e+01 2.069e+01 1.436 0.1525
## rank(MR1):rank(MR2) -5.570e-04 9.374e-04 -0.594 0.5530
## rank(MR1):rank(MR3) -4.646e-04 6.745e-04 -0.689 0.4917
## rank(MR2):rank(MR3) -1.573e-04 5.681e-04 -0.277 0.7822
## rank(MR1):conditionrobot -3.059e-01 1.615e-01 -1.894 0.0595
## rank(MR2):conditionrobot -2.012e-01 1.314e-01 -1.532 0.1271
## rank(MR3):conditionrobot 1.935e-02 1.367e-01 0.142 0.8875
## rank(MR1):rank(MR2):conditionrobot 1.935e-03 9.374e-04 2.064 0.0402
## rank(MR1):rank(MR3):conditionrobot 2.734e-04 6.745e-04 0.405 0.6857
## rank(MR2):rank(MR3):conditionrobot -4.397e-04 5.681e-04 -0.774 0.4398
##
## (Intercept)
## rank(MR1)
## rank(MR2) *
## rank(MR3)
## conditionrobot
## rank(MR1):rank(MR2)
## rank(MR1):rank(MR3)
## rank(MR2):rank(MR3)
## rank(MR1):conditionrobot .
## rank(MR2):conditionrobot
## rank(MR3):conditionrobot
## rank(MR1):rank(MR2):conditionrobot *
## rank(MR1):rank(MR3):conditionrobot
## rank(MR2):rank(MR3):conditionrobot
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.11 on 218 degrees of freedom
## Multiple R-squared: 0.1693, Adjusted R-squared: 0.1198
## F-statistic: 3.418 on 13 and 218 DF, p-value: 8.086e-05
##
## Call:
## lm(formula = concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3))^3) *
## condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -44.620 -14.770 -4.455 12.244 70.147
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -6.366e+01 3.263e+01 -1.951
## rank(MR1) 6.738e-01 3.274e-01 2.058
## rank(MR2) 5.181e-01 2.172e-01 2.385
## rank(MR3) 2.617e-01 2.724e-01 0.961
## conditionrobot -1.376e+01 3.263e+01 -0.422
## rank(MR1):rank(MR2) -3.692e-03 2.102e-03 -1.756
## rank(MR1):rank(MR3) -3.426e-03 2.150e-03 -1.593
## rank(MR2):rank(MR3) -1.350e-03 1.800e-03 -0.750
## rank(MR1):conditionrobot 2.284e-01 3.274e-01 0.698
## rank(MR2):conditionrobot 9.237e-02 2.172e-01 0.425
## rank(MR3):conditionrobot 3.337e-01 2.724e-01 1.225
## rank(MR1):rank(MR2):rank(MR3) 1.968e-05 1.408e-05 1.398
## rank(MR1):rank(MR2):conditionrobot -1.587e-03 2.102e-03 -0.755
## rank(MR1):rank(MR3):conditionrobot -3.377e-03 2.150e-03 -1.570
## rank(MR2):rank(MR3):conditionrobot -2.636e-03 1.800e-03 -1.465
## rank(MR1):rank(MR2):rank(MR3):conditionrobot 2.479e-05 1.408e-05 1.760
## Pr(>|t|)
## (Intercept) 0.0524 .
## rank(MR1) 0.0408 *
## rank(MR2) 0.0179 *
## rank(MR3) 0.3377
## conditionrobot 0.6736
## rank(MR1):rank(MR2) 0.0805 .
## rank(MR1):rank(MR3) 0.1126
## rank(MR2):rank(MR3) 0.4539
## rank(MR1):conditionrobot 0.4860
## rank(MR2):conditionrobot 0.6711
## rank(MR3):conditionrobot 0.2220
## rank(MR1):rank(MR2):rank(MR3) 0.1636
## rank(MR1):rank(MR2):conditionrobot 0.4511
## rank(MR1):rank(MR3):conditionrobot 0.1178
## rank(MR2):rank(MR3):conditionrobot 0.1444
## rank(MR1):rank(MR2):rank(MR3):conditionrobot 0.0798 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.03 on 216 degrees of freedom
## Multiple R-squared: 0.1827, Adjusted R-squared: 0.126
## F-statistic: 3.219 on 15 and 216 DF, p-value: 8.043e-05
##
## Call:
## lm(formula = concern_general ~ ((rank(MR1) + rank(MR2) + rank(MR3) +
## condition)^3), data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.483 -14.858 -5.045 11.512 68.925
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.563e+01 3.113e+01 -1.466 0.144
## rank(MR1) 3.337e-01 2.656e-01 1.256 0.210
## rank(MR2) 4.035e-01 2.082e-01 1.938 0.054
## rank(MR3) 2.400e-01 2.735e-01 0.878 0.381
## conditionrobot 3.069e+01 2.077e+01 1.478 0.141
## rank(MR1):rank(MR2) -1.498e-03 1.701e-03 -0.881 0.380
## rank(MR1):rank(MR3) -1.648e-03 1.908e-03 -0.864 0.389
## rank(MR1):conditionrobot -2.554e-01 1.787e-01 -1.429 0.155
## rank(MR2):rank(MR3) -1.296e-03 1.808e-03 -0.717 0.474
## rank(MR2):conditionrobot -2.116e-01 1.325e-01 -1.597 0.112
## rank(MR3):conditionrobot -4.514e-02 1.679e-01 -0.269 0.788
## rank(MR1):rank(MR2):rank(MR3) 8.345e-06 1.258e-05 0.663 0.508
## rank(MR1):rank(MR2):conditionrobot 1.627e-03 1.047e-03 1.554 0.122
## rank(MR1):rank(MR3):conditionrobot 2.162e-04 6.808e-04 0.318 0.751
## rank(MR2):rank(MR3):conditionrobot 6.266e-05 9.472e-04 0.066 0.947
##
## (Intercept)
## rank(MR1)
## rank(MR2) .
## rank(MR3)
## conditionrobot
## rank(MR1):rank(MR2)
## rank(MR1):rank(MR3)
## rank(MR1):conditionrobot
## rank(MR2):rank(MR3)
## rank(MR2):conditionrobot
## rank(MR3):conditionrobot
## rank(MR1):rank(MR2):rank(MR3)
## rank(MR1):rank(MR2):conditionrobot
## rank(MR1):rank(MR3):conditionrobot
## rank(MR2):rank(MR3):conditionrobot
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.14 on 217 degrees of freedom
## Multiple R-squared: 0.171, Adjusted R-squared: 0.1175
## F-statistic: 3.197 on 14 and 217 DF, p-value: 0.0001332

## Analysis of Variance Table
##
## Model 1: concern_general ~ log(MR1 + 10) + log(MR2 + 10) + log(MR3 + 10) +
## condition
## Model 2: concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + log(MR3 +
## 10)) * condition
## Model 3: concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + log(MR3 +
## 10) + condition)^2
## Model 4: concern_general ~ (log(MR1 + 10) + log(MR2 + 10) + log(MR3 +
## 10))^2 * condition
## Model 5: concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) + log(MR3 +
## 10))^3) * condition
## Model 6: concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) + log(MR3 +
## 10) + condition)^3)
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 226 110999
## 2 223 110898 3 100.9 0.0709 0.975439
## 3 220 104136 3 6761.9 4.7508 0.003143 **
## 4 217 103705 3 431.1 0.3029 0.823277
## 5 215 102004 2 1701.3 1.7930 0.168947
## 6 216 102349 -1 -345.9 0.7290 0.394157
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Call:
## lm(formula = concern_general ~ log(MR1 + 10) + log(MR2 + 10) +
## log(MR3 + 10) + condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.395 -16.078 -4.117 10.382 73.687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -268.4229 75.2895 -3.565 0.000444 ***
## log(MR1 + 10) 67.4818 33.4439 2.018 0.044799 *
## log(MR2 + 10) 61.0595 16.5333 3.693 0.000278 ***
## log(MR3 + 10) -0.9552 17.2603 -0.055 0.955918
## conditionrobot -0.1739 3.4054 -0.051 0.959320
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.16 on 226 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1324, Adjusted R-squared: 0.117
## F-statistic: 8.62 on 4 and 226 DF, p-value: 1.716e-06
##
## Call:
## lm(formula = concern_general ~ (log(MR1 + 10) + log(MR2 + 10) +
## log(MR3 + 10)) * condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -43.800 -16.129 -4.158 10.312 73.740
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -291.035 98.368 -2.959 0.00342 **
## log(MR1 + 10) 83.143 63.375 1.312 0.19089
## log(MR2 + 10) 54.180 27.012 2.006 0.04609 *
## log(MR3 + 10) 1.002 28.087 0.036 0.97158
## conditionrobot -42.589 98.368 -0.433 0.66547
## log(MR1 + 10):conditionrobot 24.415 63.375 0.385 0.70042
## log(MR2 + 10):conditionrobot -4.795 27.012 -0.178 0.85926
## log(MR3 + 10):conditionrobot -0.614 28.087 -0.022 0.98258
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 22.3 on 223 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1332, Adjusted R-squared: 0.106
## F-statistic: 4.894 on 7 and 223 DF, p-value: 3.698e-05
##
## Call:
## lm(formula = concern_general ~ (log(MR1 + 10) + log(MR2 + 10) +
## log(MR3 + 10) + condition)^2, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.752 -14.595 -4.393 11.230 68.947
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6093.82 2458.18 -2.479 0.013927 *
## log(MR1 + 10) 3021.78 1132.58 2.668 0.008197 **
## log(MR2 + 10) 2713.99 789.50 3.438 0.000702 ***
## log(MR3 + 10) -643.70 956.62 -0.673 0.501722
## conditionrobot -26.21 98.37 -0.266 0.790166
## log(MR1 + 10):log(MR2 + 10) -1334.25 382.69 -3.486 0.000591 ***
## log(MR1 + 10):log(MR3 + 10) 95.48 286.67 0.333 0.739401
## log(MR1 + 10):conditionrobot 112.06 69.38 1.615 0.107697
## log(MR2 + 10):log(MR3 + 10) 183.27 290.03 0.632 0.528104
## log(MR2 + 10):conditionrobot -109.43 41.71 -2.624 0.009301 **
## log(MR3 + 10):conditionrobot 13.55 36.35 0.373 0.709723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.76 on 220 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.186, Adjusted R-squared: 0.149
## F-statistic: 5.028 on 10 and 220 DF, p-value: 1.384e-06
##
## Call:
## lm(formula = concern_general ~ (log(MR1 + 10) + log(MR2 + 10) +
## log(MR3 + 10))^2 * condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -48.380 -14.461 -4.933 11.436 69.285
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -8278.11 4217.31 -1.963
## log(MR1 + 10) 3685.53 2034.84 1.811
## log(MR2 + 10) 3526.34 1242.98 2.837
## log(MR3 + 10) -234.07 1525.96 -0.153
## conditionrobot -2958.42 4217.31 -0.701
## log(MR1 + 10):log(MR2 + 10) -1559.42 451.98 -3.450
## log(MR1 + 10):log(MR3 + 10) 45.01 729.76 0.062
## log(MR2 + 10):log(MR3 + 10) 52.42 367.99 0.142
## log(MR1 + 10):conditionrobot 871.50 2034.84 0.428
## log(MR2 + 10):conditionrobot 935.32 1242.98 0.752
## log(MR3 + 10):conditionrobot 747.53 1525.96 0.490
## log(MR1 + 10):log(MR2 + 10):conditionrobot -226.85 451.98 -0.502
## log(MR1 + 10):log(MR3 + 10):conditionrobot -91.44 729.76 -0.125
## log(MR2 + 10):log(MR3 + 10):conditionrobot -230.42 367.99 -0.626
## Pr(>|t|)
## (Intercept) 0.050937 .
## log(MR1 + 10) 0.071491 .
## log(MR2 + 10) 0.004985 **
## log(MR3 + 10) 0.878230
## conditionrobot 0.483746
## log(MR1 + 10):log(MR2 + 10) 0.000673 ***
## log(MR1 + 10):log(MR3 + 10) 0.950872
## log(MR2 + 10):log(MR3 + 10) 0.886867
## log(MR1 + 10):conditionrobot 0.668865
## log(MR2 + 10):conditionrobot 0.452579
## log(MR3 + 10):conditionrobot 0.624719
## log(MR1 + 10):log(MR2 + 10):conditionrobot 0.616244
## log(MR1 + 10):log(MR3 + 10):conditionrobot 0.900405
## log(MR2 + 10):log(MR3 + 10):conditionrobot 0.531875
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.86 on 217 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.1894, Adjusted R-squared: 0.1408
## F-statistic: 3.9 on 13 and 217 DF, p-value: 1.09e-05
##
## Call:
## lm(formula = concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) +
## log(MR3 + 10))^3) * condition, data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.07 -15.15 -4.49 10.45 68.47
##
## Coefficients:
## Estimate
## (Intercept) -202917
## log(MR1 + 10) 89596
## log(MR2 + 10) 87469
## log(MR3 + 10) 84278
## conditionrobot -100257
## log(MR1 + 10):log(MR2 + 10) -38599
## log(MR1 + 10):log(MR3 + 10) -37247
## log(MR2 + 10):log(MR3 + 10) -36390
## log(MR1 + 10):conditionrobot 45842
## log(MR2 + 10):conditionrobot 42247
## log(MR3 + 10):conditionrobot 42505
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10) 16076
## log(MR1 + 10):log(MR2 + 10):conditionrobot -19331
## log(MR1 + 10):log(MR3 + 10):conditionrobot -19411
## log(MR2 + 10):log(MR3 + 10):conditionrobot -17951
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot 8203
## Std. Error
## (Intercept) 115903
## log(MR1 + 10) 51666
## log(MR2 + 10) 49811
## log(MR3 + 10) 50196
## conditionrobot 115903
## log(MR1 + 10):log(MR2 + 10) 22191
## log(MR1 + 10):log(MR3 + 10) 22371
## log(MR2 + 10):log(MR3 + 10) 21570
## log(MR1 + 10):conditionrobot 51666
## log(MR2 + 10):conditionrobot 49811
## log(MR3 + 10):conditionrobot 50196
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10) 9607
## log(MR1 + 10):log(MR2 + 10):conditionrobot 22191
## log(MR1 + 10):log(MR3 + 10):conditionrobot 22371
## log(MR2 + 10):log(MR3 + 10):conditionrobot 21570
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot 9607
## t value Pr(>|t|)
## (Intercept) -1.751 0.0814
## log(MR1 + 10) 1.734 0.0843
## log(MR2 + 10) 1.756 0.0805
## log(MR3 + 10) 1.679 0.0946
## conditionrobot -0.865 0.3880
## log(MR1 + 10):log(MR2 + 10) -1.739 0.0834
## log(MR1 + 10):log(MR3 + 10) -1.665 0.0974
## log(MR2 + 10):log(MR3 + 10) -1.687 0.0930
## log(MR1 + 10):conditionrobot 0.887 0.3759
## log(MR2 + 10):conditionrobot 0.848 0.3973
## log(MR3 + 10):conditionrobot 0.847 0.3981
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10) 1.673 0.0957
## log(MR1 + 10):log(MR2 + 10):conditionrobot -0.871 0.3847
## log(MR1 + 10):log(MR3 + 10):conditionrobot -0.868 0.3865
## log(MR2 + 10):log(MR3 + 10):conditionrobot -0.832 0.4062
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot 0.854 0.3942
##
## (Intercept) .
## log(MR1 + 10) .
## log(MR2 + 10) .
## log(MR3 + 10) .
## conditionrobot
## log(MR1 + 10):log(MR2 + 10) .
## log(MR1 + 10):log(MR3 + 10) .
## log(MR2 + 10):log(MR3 + 10) .
## log(MR1 + 10):conditionrobot
## log(MR2 + 10):conditionrobot
## log(MR3 + 10):conditionrobot
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10) .
## log(MR1 + 10):log(MR2 + 10):conditionrobot
## log(MR1 + 10):log(MR3 + 10):conditionrobot
## log(MR2 + 10):log(MR3 + 10):conditionrobot
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10):conditionrobot
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.78 on 215 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2027, Adjusted R-squared: 0.1471
## F-statistic: 3.644 on 15 and 215 DF, p-value: 1.145e-05
##
## Call:
## lm(formula = concern_general ~ ((log(MR1 + 10) + log(MR2 + 10) +
## log(MR3 + 10) + condition)^3), data = d_moral_merged)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.863 -14.966 -5.071 10.842 68.319
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -122654.7 67756.7 -1.810
## log(MR1 + 10) 53355.2 29437.6 1.812
## log(MR2 + 10) 53115.8 29346.4 1.810
## log(MR3 + 10) 49624.8 29518.7 1.681
## conditionrobot -1365.5 4303.7 -0.317
## log(MR1 + 10):log(MR2 + 10) -23088.3 12737.1 -1.813
## log(MR1 + 10):log(MR3 + 10) -21596.7 12816.5 -1.685
## log(MR1 + 10):conditionrobot 1764.8 2093.9 0.843
## log(MR2 + 10):log(MR3 + 10) -21562.7 12785.4 -1.687
## log(MR2 + 10):conditionrobot -265.1 1426.7 -0.186
## log(MR3 + 10):conditionrobot -329.9 1647.6 -0.200
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10) 9379.6 5545.8 1.691
## log(MR1 + 10):log(MR2 + 10):conditionrobot -387.6 460.0 -0.843
## log(MR1 + 10):log(MR3 + 10):conditionrobot -320.3 739.1 -0.433
## log(MR2 + 10):log(MR3 + 10):conditionrobot 460.0 548.5 0.839
## Pr(>|t|)
## (Intercept) 0.0717 .
## log(MR1 + 10) 0.0713 .
## log(MR2 + 10) 0.0717 .
## log(MR3 + 10) 0.0942 .
## conditionrobot 0.7513
## log(MR1 + 10):log(MR2 + 10) 0.0713 .
## log(MR1 + 10):log(MR3 + 10) 0.0934 .
## log(MR1 + 10):conditionrobot 0.4003
## log(MR2 + 10):log(MR3 + 10) 0.0931 .
## log(MR2 + 10):conditionrobot 0.8528
## log(MR3 + 10):conditionrobot 0.8415
## log(MR1 + 10):log(MR2 + 10):log(MR3 + 10) 0.0922 .
## log(MR1 + 10):log(MR2 + 10):conditionrobot 0.4004
## log(MR1 + 10):log(MR3 + 10):conditionrobot 0.6652
## log(MR2 + 10):log(MR3 + 10):conditionrobot 0.4026
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 21.77 on 216 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.2, Adjusted R-squared: 0.1481
## F-statistic: 3.857 on 14 and 216 DF, p-value: 7.403e-06
## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).

Start messing with specific moral concern
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Warning: Some predictor variables are on very different scales: consider
## rescaling
## Data: d_moral_merged3
## Models:
## r01: concern_score ~ concern_type + (1 | subid) + (1 | concern_item)
## r02: concern_score ~ concern_type + scale(rank(MR1), scale = F) +
## r02: scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F) +
## r02: (1 | subid) + (1 | concern_item)
## r03: concern_score ~ concern_type + scale(rank(MR1), scale = F) +
## r03: scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F) +
## r03: concern_type:scale(rank(MR1), scale = F) + concern_type:scale(rank(MR2),
## r03: scale = F) + concern_type:scale(rank(MR3), scale = F) + (1 |
## r03: subid) + (1 | concern_item)
## r04: concern_score ~ (concern_type + scale(rank(MR1), scale = F) +
## r04: scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F))^2 +
## r04: (1 | subid) + (1 | concern_item)
## r05: concern_score ~ (concern_type + scale(rank(MR1), scale = F) +
## r05: scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F))^2 *
## r05: condition + (1 | subid) + (1 | concern_item)
## Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
## r01 7 22431 22471 -11208 22417
## r02 10 22380 22438 -11180 22360 57.008 3 2.560e-12 ***
## r03 19 22142 22253 -11052 22104 255.217 9 < 2.2e-16 ***
## r04 22 22147 22275 -11052 22103 1.357 3 0.7157
## r05 41 22090 22329 -11004 22008 95.448 19 3.542e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Linear mixed model fit by REML ['lmerMod']
## Formula: concern_score ~ (concern_type + scale(rank(MR1), scale = F) +
## scale(rank(MR2), scale = F) + scale(rank(MR3), scale = F))^2 *
## condition + (1 | subid) + (1 | concern_item)
## Data: d_moral_merged3
##
## REML criterion at convergence: 22409.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1678 -0.5485 0.0033 0.4347 4.0360
##
## Random effects:
## Groups Name Variance Std.Dev.
## subid (Intercept) 322.181 17.949
## concern_item (Intercept) 2.824 1.681
## Residual 283.526 16.838
## Number of obs: 2526, groups: subid, 232; concern_item, 11
##
## Fixed effects:
## Estimate
## (Intercept) 1.264e+01
## concern_typephy 9.662e+00
## concern_typesoc -4.012e+00
## concern_typeper -1.244e+00
## scale(rank(MR1), scale = F) 1.543e-02
## scale(rank(MR2), scale = F) 1.645e-02
## scale(rank(MR3), scale = F) -7.207e-03
## conditionrobot 5.505e+00
## concern_typephy:scale(rank(MR1), scale = F) 7.386e-03
## concern_typesoc:scale(rank(MR1), scale = F) -8.172e-03
## concern_typeper:scale(rank(MR1), scale = F) -1.565e-03
## concern_typephy:scale(rank(MR2), scale = F) 2.244e-03
## concern_typesoc:scale(rank(MR2), scale = F) -8.029e-04
## concern_typeper:scale(rank(MR2), scale = F) -8.776e-04
## concern_typephy:scale(rank(MR3), scale = F) 1.561e-03
## concern_typesoc:scale(rank(MR3), scale = F) 3.436e-04
## concern_typeper:scale(rank(MR3), scale = F) 3.093e-04
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F) 6.809e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F) 5.527e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F) -1.591e-06
## concern_typephy:conditionrobot -2.456e+00
## concern_typesoc:conditionrobot 9.224e-01
## concern_typeper:conditionrobot -2.254e+00
## scale(rank(MR1), scale = F):conditionrobot -1.318e-02
## scale(rank(MR2), scale = F):conditionrobot 4.322e-03
## scale(rank(MR3), scale = F):conditionrobot 8.956e-03
## concern_typephy:scale(rank(MR1), scale = F):conditionrobot -9.396e-03
## concern_typesoc:scale(rank(MR1), scale = F):conditionrobot 7.024e-03
## concern_typeper:scale(rank(MR1), scale = F):conditionrobot 2.271e-03
## concern_typephy:scale(rank(MR2), scale = F):conditionrobot 4.066e-03
## concern_typesoc:scale(rank(MR2), scale = F):conditionrobot -4.886e-04
## concern_typeper:scale(rank(MR2), scale = F):conditionrobot -1.751e-03
## concern_typephy:scale(rank(MR3), scale = F):conditionrobot -7.092e-04
## concern_typesoc:scale(rank(MR3), scale = F):conditionrobot 8.290e-04
## concern_typeper:scale(rank(MR3), scale = F):conditionrobot -2.380e-03
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F):conditionrobot 9.163e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F):conditionrobot -3.516e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F):conditionrobot 7.767e-07
## Std. Error
## (Intercept) 4.621e+00
## concern_typephy 1.774e+00
## concern_typesoc 1.774e+00
## concern_typeper 1.780e+00
## scale(rank(MR1), scale = F) 5.631e-03
## scale(rank(MR2), scale = F) 4.476e-03
## scale(rank(MR3), scale = F) 4.700e-03
## conditionrobot 4.592e+00
## concern_typephy:scale(rank(MR1), scale = F) 2.024e-03
## concern_typesoc:scale(rank(MR1), scale = F) 2.024e-03
## concern_typeper:scale(rank(MR1), scale = F) 2.030e-03
## concern_typephy:scale(rank(MR2), scale = F) 9.655e-04
## concern_typesoc:scale(rank(MR2), scale = F) 9.645e-04
## concern_typeper:scale(rank(MR2), scale = F) 9.711e-04
## concern_typephy:scale(rank(MR3), scale = F) 1.246e-03
## concern_typesoc:scale(rank(MR3), scale = F) 1.245e-03
## concern_typeper:scale(rank(MR3), scale = F) 1.248e-03
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F) 6.539e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F) 4.705e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F) 3.963e-06
## concern_typephy:conditionrobot 1.553e+00
## concern_typesoc:conditionrobot 1.553e+00
## concern_typeper:conditionrobot 1.560e+00
## scale(rank(MR1), scale = F):conditionrobot 5.631e-03
## scale(rank(MR2), scale = F):conditionrobot 4.476e-03
## scale(rank(MR3), scale = F):conditionrobot 4.700e-03
## concern_typephy:scale(rank(MR1), scale = F):conditionrobot 2.024e-03
## concern_typesoc:scale(rank(MR1), scale = F):conditionrobot 2.024e-03
## concern_typeper:scale(rank(MR1), scale = F):conditionrobot 2.030e-03
## concern_typephy:scale(rank(MR2), scale = F):conditionrobot 9.655e-04
## concern_typesoc:scale(rank(MR2), scale = F):conditionrobot 9.645e-04
## concern_typeper:scale(rank(MR2), scale = F):conditionrobot 9.711e-04
## concern_typephy:scale(rank(MR3), scale = F):conditionrobot 1.246e-03
## concern_typesoc:scale(rank(MR3), scale = F):conditionrobot 1.245e-03
## concern_typeper:scale(rank(MR3), scale = F):conditionrobot 1.248e-03
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F):conditionrobot 6.539e-06
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F):conditionrobot 4.705e-06
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F):conditionrobot 3.963e-06
## t value
## (Intercept) 2.736
## concern_typephy 5.446
## concern_typesoc -2.262
## concern_typeper -0.699
## scale(rank(MR1), scale = F) 2.740
## scale(rank(MR2), scale = F) 3.675
## scale(rank(MR3), scale = F) -1.533
## conditionrobot 1.199
## concern_typephy:scale(rank(MR1), scale = F) 3.649
## concern_typesoc:scale(rank(MR1), scale = F) -4.038
## concern_typeper:scale(rank(MR1), scale = F) -0.771
## concern_typephy:scale(rank(MR2), scale = F) 2.325
## concern_typesoc:scale(rank(MR2), scale = F) -0.832
## concern_typeper:scale(rank(MR2), scale = F) -0.904
## concern_typephy:scale(rank(MR3), scale = F) 1.252
## concern_typesoc:scale(rank(MR3), scale = F) 0.276
## concern_typeper:scale(rank(MR3), scale = F) 0.248
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F) 1.041
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F) 1.175
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F) -0.401
## concern_typephy:conditionrobot -1.582
## concern_typesoc:conditionrobot 0.594
## concern_typeper:conditionrobot -1.445
## scale(rank(MR1), scale = F):conditionrobot -2.341
## scale(rank(MR2), scale = F):conditionrobot 0.966
## scale(rank(MR3), scale = F):conditionrobot 1.905
## concern_typephy:scale(rank(MR1), scale = F):conditionrobot -4.642
## concern_typesoc:scale(rank(MR1), scale = F):conditionrobot 3.471
## concern_typeper:scale(rank(MR1), scale = F):conditionrobot 1.119
## concern_typephy:scale(rank(MR2), scale = F):conditionrobot 4.211
## concern_typesoc:scale(rank(MR2), scale = F):conditionrobot -0.507
## concern_typeper:scale(rank(MR2), scale = F):conditionrobot -1.804
## concern_typephy:scale(rank(MR3), scale = F):conditionrobot -0.569
## concern_typesoc:scale(rank(MR3), scale = F):conditionrobot 0.666
## concern_typeper:scale(rank(MR3), scale = F):conditionrobot -1.908
## scale(rank(MR1), scale = F):scale(rank(MR2), scale = F):conditionrobot 1.401
## scale(rank(MR1), scale = F):scale(rank(MR3), scale = F):conditionrobot -0.747
## scale(rank(MR2), scale = F):scale(rank(MR3), scale = F):conditionrobot 0.196
## fit warnings:
## Some predictor variables are on very different scales: consider rescaling